Publications

Kanazawa (2008), Templer (2008), and Templer and Arikawa (2006) claimed to have found empirical support for evolutionary theories of race differences in intelligence by correlating estimates of national IQ with indicators of reproductive strategies, temperature, and geographic distance from Africa. In this paper we criticize these studies on methodological, climatic, and historical grounds. We show that these studies assume that the Flynn Effect is either nonexistent or invariant with respect to different regions of the world, that there have been no migrations and climatic changes over the course of evolution, and that there have been no trends over the last century in indicators of reproductive strategies (e.g., declines in fertility and infant mortality). In addition, we show that national IQs are strongly confounded with the current developmental status of countries. National IQs correlate with all the variables that have been suggested to have caused the Flynn Effect in the developed world.

The veracity of substantive research claims hinges on the way experimental data are collected and analyzed. In this article, we discuss an uncomfortable fact that threatens the core of psychology's academic enterprise: almost without exception, psychologists do not commit themselves to a method of data analysis before they see the actual data. It then becomes tempting to fine tune the analysis to the data in order to obtain a desired result-a procedure that invalidates the interpretation of the common statistical tests. The extent of the fine tuning varies widely across experiments and experimenters but is almost impossible for reviewers and readers to gauge. To remedy the situation, we propose that researchers preregister their studies and indicate in advance the analyses they intend to conduct. Only these analyses deserve the label "confirmatory," and only for these analyses are the common statistical tests valid. Other analyses can be carried out but these should be labeled "exploratory." We illustrate our proposal with a confirmatory replication attempt of a study on extrasensory perception.

According to Kane (this issue), “the validity of a proposed interpretation or use depends on how well the evidence supports” the claims being made. Because truth and evidence are distinct, this means that the validity of a test score interpretation could be high even though the interpretation is false. As an illustration, we discuss the case of phlogiston measurement as it existed in the 18th century. At face value, Kane's theory would seem to imply that interpretations of phlogiston measurement were valid in the 18th century (because the evidence for them was strong), even though amounts of phlogiston do not exist and hence cannot be measured. We suggest that this neglects an important aspect of validity and suggest various ways in which Kane's theory could meet this challenge.

Pairwise network models such as the Gaussian Graphical Model (GGM) are a powerful and intuitive way to analyze dependencies in multivariate data. A key assumption of the GGM is that each pairwise interaction is independent of the values of all other variables. However, in psychological research this is often implausible. In this paper, we extend the GGM by allowing each pairwise interaction between two variables to be moderated by (a subset of) all other variables in the model, and thereby introduce a Moderated Network Model (MNM). We show how to construct the MNM and propose an L1-regularized nodewise regression approach to estimate it. We provide performance results in a simulation study and show that MNMs outperform the split-sample based methods Network Comparison Test (NCT) and Fused Graphical Lasso (FGL) in detecting moderation effects. Finally, we provide a fully reproducible tutorial on how to estimate MNMs with the R-package mgm and discuss possible issues with model misspecification.

We argue that neural networks for semantic cognition, as proposed by Rogers & McClelland (R&M), do not acquire semantics and therefore cannot be the basis for a theory of semantic cognition. The reason is that the neural networks simply perform statistical categorization procedures, and these do not require any semantics for their successful operation. We conclude that this has severe consequences for the semantic cognition views of R&M.

Causal interpretations of measurement models carry advantages in terms of understanding manipulations of the construct, having reason to expect probabilistic

creases from 515 to 344) and the strong regularization penalty; both networks lose almost all of their connections and, in that trivial sense, become more alike.As shown in our article, 1 when using procedures that have less effect on power (like partialling out general level of functioning or weakening the regularization parameter), differences between groups become more, rather than less, pronounced.Although we believe that it is not very likely that the associations between symptoms are substantially different for individual patients, intraindividual analyses are needed to test this.In addition, intraindividual analyses are warranted to determine whether symptoms are associated over time within patients.Therefore, we gladly reveal that the Netherlands Study of Depression and Anxiety, 3 from which we drew our sample, recently started a new wave of measures in which 400 of its nearly 3000 participants are studied with Ecological Momentary Assessment 4 over 2 weeks.The aim of this study is to provide more insight into the association between intraindividual and interindividual differences, which will lead to an increased understanding of how nomothetic and idiographic analyses are related.

Network approaches have been proposed as an alternative way of thinking about relations between symptoms of mental disorders. Unlike traditional psychometric approaches, network models view these associations as the result of direct interactions between symptoms. Disorders are defined as alternative stable states of a network due to increased connectivity between symptoms. This increased connectivity creates a pattern of reinforcement, so the system can get stuck in a state of prolonged activation. Mental health is defined as the stable state of a weakly connected network. Although symptomatology may be temporarily increased in a healthy network (e.g., due to adverse life events), as the influence of a shock wanes the network will spontaneously return to its healthy state. Strongly connected networks, however, may transition into disordered states upon similar external shocks, and may not naturally recover. Thus, the proposed definitions yield plausible conceptualizations of resilience and vulnerability.

Scientific theories can be viewed as attempts to explain phenomena by showing how they would arise, if certain assumptions concerning the structure of the world were true. Such theories invariably involve a reference to theoretical entities and attributes. Theoretical attributes include such things as electrical charge and distance in physics, inclusive fitness and selective pressure in biology, brain activity and anatomic structure in neuroscience, and intelligence and developmental stages in psychology. These attributes are not subject to direct observation but require an inferential process by which the researcher infers positions of objects on the attribute on the basis of a set of observations.

Emotions are part and parcel of the human condition, but their nature is debated. Three broad classes of theories about the nature of emotions can be distinguished: affect program theories, constructionist theories, and appraisal theories. Integrating them in a unifying theory is challenging. An integrative psychometric model of emotions can inform such a theory, because psychometric models are intertwined with theoretical perspectives about constructs. To identify an integrative psychometric model, we (a) delineate properties of emotions stated by emotion theories, and (b) investigate whether psychometric models account for these properties. Specifically, an integrative psychometric model of emotions should allow identifying distinct emotions (central in affect program theories), should allow between and within person variation of emotions (central in constructionist theories), and should allow causal relationships between emotion components (central in appraisal theories). Evidence suggests that the popular reflective and formative latent variable models—in which emotions are conceptualized as unobservable causes or consequences of emotion components—cannot account for all properties. Conversely, a psychometric network model—in which emotions are conceptualized as systems of causally interacting emotion components—accounts for all properties. The psychometric network model thus constitutes an integrative psychometric model of emotions, facilitating progress toward a unifying theory.

The possibility or impossibility of quantitative measurement in psychology has important ramifications for the nature of psychology as a discipline. Trendler’s (2009) argument for the impossibility of psychological measurement suggests a general and potentially fruitful strategy for further research on this question. However, the specific argument offered by Trendler appears flawed in several respects. It seems to conflate what must hold true with what one must know and also equivocate on the necessary evidence. Moreover, if the argument supported its conclusion, it would rule out qualitative discourse on psychology as well as psychological measurement. Taking Trendler’s argument as an example, one can formulate a general structure to arguments adopting the same basic strategy. An overview of the requirements that such arguments should meet provides a metatheoretical perspective that can assist authors in constructing such arguments and readers in critically evaluating them.